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Damages in critical infrastructure occur abruptly, and disruptions evolve with time dynamically. Understanding the situation of critical infrastructure disruptions is essential to effective disaster response and recovery of communities. Although the potential of social media data for situation awareness during disasters has been investigated in recent studies, the application of social sensing in detecting disruptions and analyzing evolutions of the situation about critical infrastructure is limited. To address this limitation, this study developed a graph‐based method for detecting credible situation information related to infrastructure disruptions in disasters. The proposed method was composed of data filtering, burst time‐frame detection, content similarity calculation, graph analysis, and situation evolution analysis. The application of the proposed method was demonstrated in a case study of Hurricane Harvey in 2017 in Houston. The findings highlighted the capability of the proposed method in detecting credible situational information and capturing the temporal and spatial patterns of critical infrastructure events that occurred in Harvey, including disruptive events and their adverse impacts on communities. The proposed methodology can improve the ability of community members, volunteer responders, and decision makers to detect and respond to infrastructure disruptions in disasters.

 

The objective of this paper is to establish a meta-network framework to identify constituents in Disaster Management System-of-Systems (DM-SoS), conceptualize relationships and interactions among the constituents, and formulate quantitative measurements of DM-SoS performance for achieving network-centric operation and coordination in the context of disasters. With increasingly serious impacts of disasters on interdependent and heterogeneous systems, the improvement of effective and integrative disaster response and coordination is needed. However, some existing literature only proposed some frameworks for modeling disaster management systems, while another stream of studies only examined the social network analysis (SNA) for understanding the interactions between stakeholders. Thus, quantitative and integrative measurements in DM-SoS are missing. To address this knowledge gap, the authors created and discussed a metanetwork framework integrating various types of entities and relationships for quantitatively analyzing the performance of DM-SoS. First, this framework defined nodes and links in meta-metrics for abstracting constituents in disaster management. Second, some performance indicators (e.g., effectiveness, the extent of information sharing, and the extent of self-organization) were created to show the capacities of disaster systems, and the potential perturbations in disaster environment were translated by network theory. Finally, we examined the impacts of perturbations on the indicators and assessed the performance by integrating overall indicators. This study highlighted the significance of quantitative measurements and an integrative perspective on analyzing efficiency and effectiveness of disaster response and coordination. The study also provides implications for making comparisons of different response strategies for decision makers to achieve resilient disaster management systems.